Estimation of glacier surface motion by robust phase correlation and point like features of SAR intensity images

https://doi.org/10.1016/j.isprsjprs.2016.08.012Get rights and content

Abstract

For monitoring of glacier surface motion in pole and alpine areas, radar remote sensing is becoming a popular technology accounting for its specific advantages of being independent of weather conditions and sunlight. In this paper we propose a method for glacier surface motion monitoring using phase correlation (PC) based on point-like features (PLF). We carry out experiments using repeat-pass TerraSAR X-band (TSX) and Sentinel-1 C-band (S1C) intensity images of the Taku glacier in Juneau icefield located in southeast Alaska. The intensity imagery is first filtered by an improved adaptive refined Lee filter while the effect of topographic reliefs is removed via SRTM-X DEM. Then, a robust phase correlation algorithm based on singular value decomposition (SVD) and an improved random sample consensus (RANSAC) algorithm is applied to sequential PLF pairs generated by correlation using a 2D sinc function template. The approaches for glacier monitoring are validated by both simulated SAR data and real SAR data from two satellites. The results obtained from these three test datasets confirm the superiority of the proposed approach compared to standard correlation-like methods. By the use of the proposed adaptive refined Lee filter, we achieve a good balance between the suppression of noise and the preservation of local image textures. The presented phase correlation algorithm shows the accuracy of better than 0.25 pixels, when conducting matching tests using simulated SAR intensity images with strong noise. Quantitative 3D motions and velocities of the investigated Taku glacier during a repeat-pass period are obtained, which allows a comprehensive and reliable analysis for the investigation of large-scale glacier surface dynamics.

Introduction

Glaciers act as important components in albedo feedback mechanisms of the local and global climate system (Rees, 2006). Glaciers also play vital roles as crucial reservoirs of global fresh water. For instance, some climatic threshold regions (e.g. Central Asia and India) characterized by water stress and densely population are reliant at a large extent on snow and glacier melt water (Pellikka and Rees, 2010). However, in the context of global warming, the melting glaciers are expected to make increasing contributes to global sea level rise (Gardner et al., 2013). It is estimated that melting glaciers account for approximately 29% of the observed sea level rise (Schubert et al., 2013). Therefore, researches on the behavior of glaciers and the glacier mass balance with geometry changes have drawn growing attention in the past decade. The monitoring of glacier surface motion as well as its velocity is an essential method to obtain the quantitative changes of glacier providing numerical clues to deducing past glacier changes and predicting future developments (Schubert et al., 2013).

The earliest monitoring and studies of glaciers depended upon the compulsory repeated field-survey by glaciologists. The researchers had to travel to far-flung glaciers, normally located in high mountainous areas characterized by hazardous environment, to collect field data only in few dozen points accompanying with time-consuming and expensive field seasons. Accordingly, due to the inaccessibility of many glacier areas and the environmental hazards, the acquisition of reliable measuring data in large scale was always a challenging task (Schubert et al., 2013). Remotely sensed data, especially the space-borne data, providing high resolution, trustworthy, large scale, and time-sequential measurements in a cost effective manner, is expected to be competent for the glacier monitoring. Among various remote sensing techniques, SAR features the abilities of ignoring weather condition and providing images all year long with both intensity and phase information and is becoming more popular for measuring the glacier motion.

In this paper, we present an approach for the estimation of glacier surface motion using repeat-pass SAR intensity images via point like features (PLF) and a robust phase correlation (PC) algorithm. We have chosen as site for our case study the Taku glacier in Juneau ice field located in southeast Alaska. The work consists of four main steps: the preprocessing of SAR intensity imagery, the selection of PLF, the dense matching using robust PC and the estimation of 3D motion of the glacier. The main contributions which are specific to measuring the glacier surface motion can be stated as:

  • A dense matching method based on point-like features in SAR intensity images is developed for a robust workflow for estimating the dense glacier motion map.

  • An improved robust PC algorithm combing the random sample consensus (RANSAC) algorithm with singular value decomposition (SVD) is introduced for matching SAR images, which can cope with strong speckles and noise while keeping a good matching accuracy between small patches.

  • An adaptive refined version of Lee filter is developed for the despeckling of SAR images, while the sharpness and details of image patterns can be preserved.

  • The feasibility and superiority of proposed approach motion from high resolution SAR are demonstrated and endorsed by both synthetic and real datasets.

This paper is structured as follows. Section 2 introduces related works in the field of glacier monitoring using spaceborne SAR data as well as the state of art of the phase correlation algorithms. Current research dealing with the Taku glacier is also introduced in this section. In Section 3 the proposed phase correlation algorithm is presented and the processing steps to measure the glacier motion are described in detail. In Section 4, the experimental results using synthetic data and real SAR data of Taku glacier are presented and analyzed. Finally, conclusions are drawn and future perspectives are outlined in Section 5.

Section snippets

Glacier motion estimation using SAR image

The optical imagery acquired by earth observation satellites is the most commonly used dataset for glacier monitoring, featured for its high resolution, wide coverage, lower cost and shorter revisiting period than traditional method such as field surveying. Many researchers have already used optical imagery for measuring surface displacements (Schubert et al., 2013), glacier topography (Fallourd et al., 2011), and velocity (Berthier et al., 2005). However, for the polar and alpine regions, the

Workflow

An overall workflow of the proposed glacier motion estimation approach is shown in Fig. 2. The workflow consists of three main phases: pre-processing of SAR intensity image (I), dense matching of an image pair (II), and estimation of glacier surface motion (III).

In the pre-processing phase, the SAR image pair is filtered by the proposed adaptive refined Lee (ARLee) filter, so as to simultaneously reduce the speckle noise and preserve the details of texture. External DEM data are introduced for

Experiments

Both simulated and real SAR intensity image data are used to assess the performance of proposed method and to measure the real glacier motion. In simulated case, synthetic SAR intensity images are generated from original optical images with added noises. In actual application case, the TSX intensity image pairs covering Taku glacier is employed, aiming to measuring the motion of glacier surface within a time period of 11 days. In addition, in the context of glacier velocity monitoring, one of

Image filtering result

In Fig. 13, the filtering results using different filters are exemplified. Besides the proposed ARLee filter, Boxcar filter, local sigma filter, Lee filter, enhanced Lee filter and non-local mean filter are employed as comparisons. The simulated SAR intensity image is generated with a noise variance of 0.3 and noise mean of 1. The window size of filters used is 7 × 7 (for the ARLee filter, it is the minimum window size). SMPI and EPI, aforementioned evaluation criterions for the performance of

Summary and conclusion

In this paper, the proposed approaches for estimating glacier surface motion in Taku glacier of Juneau icefield using repeat SAR intensity images via PLFs and the robust PC algorithm have been proposed. The proposed approach has been validated by the both simulated and real SAR data. The results obtained under a variety of test confirm the superiority and feasibility of the proposed approaches, especially for the high-resolution TSX images. Several conclusions can be drawn, as follows:

  • The

Acknowledgments

We would like to thank Oliver Lang from Infoterra for providing us the TerraSAR-X data of the Juneau Icefield. Furthermore we thank Oliver Maksymiuk and Michael Schmitt from Photogrammetry and Remote Sensing, and Prof. Wunderlich from Geodesy, Technische Universität München for the kind directions and helps.

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